Meta-Analysis the Effect of Aerobic Exercise on The Social Well-Being in Breast Cancer Survivor
Bibliographic record
Abstract
Background: Exercise is associated with decreased recurrence risk, improved survival and quality of life for breast cancer survivors. This is remarkable in light of empirical work supporting breast cancer survivors as generally motivated to make healthy lifestyle changes, including exercise. This study aimed to invetigate the effect of aerobic exercise on the social well-being in breast cancer survivor. Subjects and Method: A systematic literature search was conducted in multiple databases including PubMed, Science Direct, and Google Scholar. The following search terms were used: "breast cancer" OR "ca mammae" OR "carcinoma mammae" OR "mammae cancer"AND aerobic OR "aerobic exercise" AND "quality of life" OR "QOL" AND "RCT" OR "randomized control trial" OR "cluster-randomized control trial". The articles were filtered using PICO model, including: (1) Population= breast cancer survivor, (2) intervention= aerobic exercise, (3) comparison= none intervention, and (4) outcome= quality of life. The inclusion criteria were full-text, randomized controlled trial, and reported mean and standard deviation. The systematic review was carried out according to the PRISMA flow diagram. The data were quantitatively examine by metaanalysis using RevMan 5.3. Results: 9 studies from California, Brazil, Kosovo, Spain, United Kingdom, Canada, Germany, and Netherland were involved for meta-analysis. The results of this study showed that aerobic exercise improved quality of life in breast cancer survivor (Standardized Mean Difference= 0.24; 95% CI= -0.01 to 0.48), and it was statistically marginally significant (p= 0.060). Conclusion: Aerobic exercise improves quality of life in breast cancer survivor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.060 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".